{"url":"https://api.github.com/gists/4341954","forks_url":"https://api.github.com/gists/4341954/forks","commits_url":"https://api.github.com/gists/4341954/commits","id":"4341954","node_id":"MDQ6R2lzdDQzNDE5NTQ=","git_pull_url":"https://gist.github.com/4341954.git","git_push_url":"https://gist.github.com/4341954.git","html_url":"https://gist.github.com/4341954","files":{".block":{"filename":".block","type":"text/plain","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/703d310b399098a243a76a50bc209167e924cfd2/.block","size":17,"truncated":false,"content":"license: gpl-3.0\n"},"README.md":{"filename":"README.md","type":"text/plain","language":"Markdown","raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/1f6f54c0d794f3835d2857135b870209c29c484d/README.md","size":1450,"truncated":false,"content":"[Kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a method of estimating the probability distribution of a random variable based on a random sample. In contrast to a [histogram](/mbostock/3048450), kernel density estimation produces a smooth estimate. The smoothness can be tuned via the kernel’s _bandwidth_ parameter. With the correct choice of bandwidth, important features of the distribution can be seen, while an incorrect choice results in undersmoothing or oversmoothing and obscured features.\n\nThis example shows a histogram and a kernel density estimation for times between eruptions of [Old Faithful Geyser](https://en.wikipedia.org/wiki/Old_Faithful) in Yellowstone National Park, taken from R’s [`faithful`](https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/faithful.html) dataset. The data follow a [bimodal distribution](https://en.wikipedia.org/wiki/Bimodal_distribution); short eruptions are followed by a wait time averaging about 55 minutes, and long eruptions by a wait time averaging about 80 minutes. In recent years, wait times have been increasing, possibly due to the effects of earthquakes on the geyser’s geohydrology.\n\nThis example is based on a Protovis version by [John Firebaugh](/jfirebaugh/900762). See also a [two-dimensional density estimation](/mbostock/e3f4376d54e02d5d43ae32a7cf0e6aa9) of this dataset using [d3-contour](https://github.com/d3/d3-contour).\n"},"faithful.json":{"filename":"faithful.json","type":"application/json","language":"JSON","raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/c03602b70ea5241bafd034638055b9c0d48eac69/faithful.json","size":818,"truncated":false,"content":"[79,54,74,62,85,55,88,85,51,85,54,84,78,47,83,52,62,84,52,79,51,47,78,69,74,83,55,76,78,79,73,77,66,80,74,52,48,80,59,90,80,58,84,58,73,83,64,53,82,59,75,90,54,80,54,83,71,64,77,81,59,84,48,82,60,92,78,78,65,73,82,56,79,71,62,76,60,78,76,83,75,82,70,65,73,88,76,80,48,86,60,90,50,78,63,72,84,75,51,82,62,88,49,83,81,47,84,52,86,81,75,59,89,79,59,81,50,85,59,87,53,69,77,56,88,81,45,82,55,90,45,83,56,89,46,82,51,86,53,79,81,60,82,77,76,59,80,49,96,53,77,77,65,81,71,70,81,93,53,89,45,86,58,78,66,76,63,88,52,93,49,57,77,68,81,81,73,50,85,74,55,77,83,83,51,78,84,46,83,55,81,57,76,84,77,81,87,77,51,78,60,82,91,53,78,46,77,84,49,83,71,80,49,75,64,76,53,94,55,76,50,82,54,75,78,79,78,78,70,79,70,54,86,50,90,54,54,77,79,64,75,47,86,63,85,82,57,82,67,74,54,83,73,73,88,80,71,83,56,79,78,84,58,83,43,60,75,81,46,90,46,74]\n"},"index.html":{"filename":"index.html","type":"text/html","language":"HTML","raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/359dbbeee68a5a5839a87283828c50e40d8cc32a/index.html","size":2329,"truncated":false,"content":"\n\n\n\n\n"},"thumbnail.png":{"filename":"thumbnail.png","type":"image/png","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/992cc6995683e3f977d4f8383386961e3fdb230d/thumbnail.png","size":9875,"truncated":false,"content":"�PNG\r\n\u001A\n\u0000\u0000\u0000\rIHDR\u0000\u0000\u0000�\u0000\u0000\u0000x\b\u0002\u0000\u0000\u0000id\u0019�\u0000\u0000\n�iCCPicm\u0000\u0000HǕ�\u0007PS�\u0016ǿ{�\u001B-\u0010\u0001)��.]z\r� 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